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Revert "[7/N] Fix Wextra-semi warning (#140225)"

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This reverts commit ffb979032dc149b4c895526fe5b92d713ed7b1e1.

Reverted https://github.com/pytorch/pytorch/pull/140225 on behalf of https://github.com/kit1980 due to breaking internal builds ([comment](https://github.com/pytorch/pytorch/pull/140225#issuecomment-2469312229))
This commit is contained in:
PyTorch MergeBot
2024-11-12 00:02:06 +00:00
parent 0af38b1034
commit dbb55b448b
27 changed files with 471 additions and 470 deletions
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7
.clang-format
View File

@ -101,16 +101,9 @@ SpacesInParentheses: false
SpacesInSquareBrackets: false
Standard: c++17
StatementMacros:
- C10_DEFINE_bool
- C10_DEFINE_int
- C10_DEFINE_int32
- C10_DEFINE_int64
- C10_DEFINE_string
- PyObject_HEAD
- PyObject_VAR_HEAD
- PyException_HEAD
- DEFINE_BINARY
TabWidth: 8
UseTab: Never
---

12
aten/src/ATen/cpu/vec/vec512/vec512_bfloat16.h
View File

@ -1594,8 +1594,8 @@ inline std::tuple<Vectorized<float>, Vectorized<float>> convert_##name##_float(c
inline Vectorized<type> convert_float_##name(const Vectorized<float>& a, const Vectorized<float>& b) { \
return cvt_from_fp32<type>(__m512(a), __m512(b)); \
}
CONVERT_VECTORIZED_INIT(BFloat16, bfloat16)
CONVERT_VECTORIZED_INIT(Half, half)
CONVERT_VECTORIZED_INIT(BFloat16, bfloat16);
CONVERT_VECTORIZED_INIT(Half, half);
#else //defined(CPU_CAPABILITY_AVX512)
@ -1624,8 +1624,8 @@ inline Vectorized<type> convert_float_##name(const Vectorized<float>& a, const V
} \
return Vectorized<type>::loadu(arr2); \
}
CONVERT_NON_VECTORIZED_INIT(BFloat16, bfloat16)
CONVERT_NON_VECTORIZED_INIT(Half, half)
CONVERT_NON_VECTORIZED_INIT(BFloat16, bfloat16);
CONVERT_NON_VECTORIZED_INIT(Half, half);
#endif // defined(CPU_CAPABILITY_AVX512)
@ -1663,8 +1663,8 @@ inline void load_fp32_from_##name(const type *data, Vectorized<float>& out1, Vec
data += Vectorized<float>::size(); \
load_fp32_from_##name(data, out2); \
}
LOAD_FP32_NON_VECTORIZED_INIT(BFloat16, bf16)
LOAD_FP32_NON_VECTORIZED_INIT(Half, fp16)
LOAD_FP32_NON_VECTORIZED_INIT(BFloat16, bf16);
LOAD_FP32_NON_VECTORIZED_INIT(Half, fp16);
#endif
}}}

2
c10/core/SymInt.cpp
View File

@ -61,6 +61,7 @@ bool SymInt::has_hint() const {
} \
}
// clang-format off
DEFINE_BINARY(operator+, std::plus<>(), add, SymInt)
DEFINE_BINARY(operator-, std::minus<>(), sub, SymInt)
DEFINE_BINARY(operator*, std::multiplies<>(), mul, SymInt)
@ -74,6 +75,7 @@ DEFINE_BINARY(sym_gt, std::greater<>(), gt, SymBool)
DEFINE_BINARY(sym_ge, std::greater_equal<>(), ge, SymBool)
DEFINE_BINARY(min, std::min, sym_min, SymInt)
DEFINE_BINARY(max, std::max, sym_max, SymInt)
// clang-format on
SymInt::operator SymFloat() const {
if (auto ma = maybe_as_int()) {

4
tools/autograd/templates/VariableType.h
View File

@ -18,7 +18,7 @@
namespace at {
struct Quantizer;
}
};
namespace torch { namespace autograd {
@ -54,6 +54,6 @@ namespace VariableType {
const at::Tensor & unpack(const Tensor & t, const char * name, int pos);
at::Tensor unpack_opt(const Tensor & t, const char * name, int pos);
std::vector<at::Tensor> unpack(const at::ITensorListRef& tl, const char *name, int pos);
}
};
}} // namespace torch::autograd

2
torch/csrc/distributed/autograd/context/context.cpp
View File

@ -21,7 +21,7 @@ std::unordered_set<rpc::worker_id_t> DistAutogradContext::getKnownWorkerIds()
const {
std::lock_guard<std::mutex> guard(lock_);
return knownWorkerIds_;
}
};
void DistAutogradContext::addKnownWorkerId(const rpc::worker_id_t workerId) {
std::lock_guard<std::mutex> guard(lock_);

2
torch/csrc/distributed/c10d/reducer.hpp
View File

@ -103,7 +103,7 @@ class TORCH_API Reducer {
// been applied.
void set_optimizer_in_backward() {
optim_in_backward_ = true;
}
};
// Runs allreduce or installed communication hook given GradBucket instance.
c10::intrusive_ptr<c10::ivalue::Future> run_comm_hook(

6
torch/csrc/jit/api/function_impl.cpp
View File

@ -16,7 +16,7 @@
C10_DEFINE_bool(
torch_jit_do_not_store_optimized_graph,
false,
"Do not store the optimized graph.")
"Do not store the optimized graph.");
namespace torch::jit {
namespace {
@ -133,8 +133,8 @@ GraphFunction::SpecializationKey GraphFunction::currentSpecialization() const {
void preoptimizeGraph(std::shared_ptr<Graph>& graph, bool disable_autocast) {
Inline(*graph);
// Peephole Optimize cleans up many "is None" checks and creates constant
// prop opportunities
// Peephole Optimize cleans up many "is None" checks and creates constant prop
// opportunities
PeepholeOptimize(graph, true);
// AliasDb construction can be slow, so run it just on immutable types

2
torch/csrc/jit/passes/canonicalize_graph_fuser_ops.cpp
View File

@ -6,7 +6,7 @@
namespace torch::jit {
struct ChunkOutput {
ChunkOutput(Value* v, size_t o) : val(v), offset(o) {}
ChunkOutput(Value* v, size_t o) : val(v), offset(o){};
Value* val;
size_t offset;
};

9
torch/csrc/jit/passes/tensorexpr_fuser.cpp
View File

@ -29,12 +29,12 @@
C10_DEFINE_bool(
torch_jit_disable_cat,
false,
"disable aten::cat in TE fusion groups")
"disable aten::cat in TE fusion groups");
C10_DEFINE_bool(
torch_jit_enable_dynamic_shape_fusion,
false,
"enable TE fusion using dynamic shapes")
"enable TE fusion using dynamic shapes");
namespace torch::jit {
@ -82,8 +82,9 @@ static const OperatorSet& supported_non_eltwise_set() {
"aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor",
"aten::matmul(Tensor self, Tensor other) -> Tensor",
};
// clang-format on
return supported_non_eltwise_set;
}
};
bool isSupported(Node* node) {
// For Block codegen we allow limited ops.
@ -101,6 +102,7 @@ bool isSupported(Node* node) {
"aten::cat(Tensor[] tensors, int dim=0) -> Tensor",
"aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a)",
};
// clang-format on
if (get_tensorexpr_elementwise_set().contains(node) ||
node->isMemberOf(supported_non_eltwise_set()) ||
@ -901,6 +903,7 @@ class TensorExprFuser {
static const OperatorSet pow{
"aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor",
};
// clang-format on
// Check types of input values.
for (const Value* v : node->inputs()) {

2
torch/csrc/jit/runtime/autodiff.cpp
View File

@ -167,7 +167,7 @@ static std::optional<std::vector<Value*>> build_script_grad(
auto grad_inputs = insertGraph(*graph, *bw_graph, grad);
grad_inputs = unpackOutputs(grad_inputs);
return grad_inputs;
}
};
namespace {
class GradientHelper {

24
torch/csrc/jit/runtime/graph_executor.cpp
View File

@ -56,9 +56,10 @@
C10_DEFINE_bool(
torch_jit_execution_plan_reuse_code_graph,
false,
"Directly reuse the preprocessed graph in the CodeImpl to reduce the memory consumption. This is aggressive memory saving, and please be cautious!")
"Directly reuse the preprocessed graph in the CodeImpl to reduce the memory consumption. This is aggressive memory saving, and please be cautious!");
namespace torch::jit {
EnableProfilingGuard::EnableProfilingGuard() {
auto& executor_mode = getExecutorMode();
old_executor_mode = executor_mode;
@ -431,8 +432,8 @@ struct DifferentiableGraphOp {
{
auto inputs = last(stack, num_inputs);
// hook up the outputs of df to the gradient functions of the inputs
// that require gradients
// hook up the outputs of df to the gradient functions of the inputs that
// require gradients
for (auto idx : grad.df_output_vjps) {
grad_fn->addOutputForIValue(inputs[idx]);
}
@ -454,8 +455,8 @@ struct DifferentiableGraphOp {
// TODO - XXX - if any output is the same tensor multiple times, views
// have to be setup here. We need to refactor autograd until it is safe
// for tensors to be constructed without all the viewing infrastructure.
// this is currently intentionally not done here so we can get an idea
// of our perf before introducing overhead for correctness
// this is currently intentionally not done here so we can get an idea of
// our perf before introducing overhead for correctness
for (auto idx : grad.df_input_vjps) {
grad_fn->addInputIValue(outputs[idx]);
}
@ -500,8 +501,7 @@ struct DifferentiableGraphOp {
detach(stack[i]);
}
}
// Capture (save) inputs that would be required to subsequently run
// backwards
// Capture (save) inputs that would be required to subsequently run backwards
void captureInputs(
DifferentiableGraphBackward& grad_fn,
at::ArrayRef<IValue> inputs) const {
@ -736,10 +736,8 @@ struct GraphExecutorImpl : public GraphExecutorImplBase {
runOptimization(opt_graph);
// Phase 4. If this graph will be differentiated, we need to slice out the
// symbolically differentiable subgraphs for further
// optimizations.
// Phase 5. Apply non-differentiable optimizations to the graphs we've
// found
// symbolically differentiable subgraphs for further optimizations.
// Phase 5. Apply non-differentiable optimizations to the graphs we've found
// (or the whole graph if we know we won't need its derivative).
if (needsGradient(opt_graph)) {
auto diff_nodes = CreateAutodiffSubgraphs(
@ -783,8 +781,8 @@ struct GraphExecutorImpl : public GraphExecutorImplBase {
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
ArgumentSpecCreator arg_spec_creator_;
// Populated only when optimize is false (and in that case plan_cache will
// be unused). The compiled version of graph.
// Populated only when optimize is false (and in that case plan_cache will be
// unused). The compiled version of graph.
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
ExecutionPlan fallback;

4
torch/csrc/jit/runtime/interpreter.cpp
View File

@ -49,12 +49,12 @@ using torch::distributed::autograd::DistAutogradContainer;
C10_DEFINE_bool(
torch_jit_enable_rethrow_caught_exception,
false,
"enable rethrowing caught exception")
"enable rethrowing caught exception");
C10_DEFINE_bool(
torch_jit_enable_expanded_stacks,
false,
"When true we will attemps to pre-expand node stacks and cache expanded stacks.")
"When true we will attemps to pre-expand node stacks and cache expanded stacks.");
namespace torch::jit {

16
torch/csrc/jit/runtime/profiling_graph_executor_impl.cpp
View File

@ -41,32 +41,32 @@
C10_DEFINE_bool(
torch_jit_enable_new_executor,
true,
"If this flag is set to false TorchScript will be using the legacy/original executor")
"If this flag is set to false TorchScript will be using the legacy/original executor");
C10_DEFINE_bool(
torch_jit_disable_warning_prints,
false,
"Disables warning.warn prints in TorchScript graph")
"Disables warning.warn prints in TorchScript graph");
C10_DEFINE_bool(
torch_jit_static_then_dynamic,
false,
"fuse on two static compilations then 10 dynamic")
"fuse on two static compilations then 10 dynamic");
C10_DEFINE_bool(
torch_jit_always_dynamic,
false,
"fuse on 12 dynamic compilations")
"fuse on 12 dynamic compilations");
C10_DEFINE_bool(
torch_jit_release_profiling_graph_after_optimization,
false,
"After getOptimizedPlanFor release the optimization record for reduction of memory in inference. This is aggressive memory saving, and please be cautious!")
"After getOptimizedPlanFor release the optimization record for reduction of memory in inference. This is aggressive memory saving, and please be cautious!");
C10_DEFINE_int32(
torch_jit_release_profiling_graph_delay_in_seconds,
60,
"How long to wait before releasing the profiling graph after optimizaiton is done. Only used if torch_jit_release_profiling_graph_after_optimization is set to true.")
"How long to wait before releasing the profiling graph after optimizaiton is done. Only used if torch_jit_release_profiling_graph_after_optimization is set to true.");
constexpr size_t kDefaultNumProfiledRuns = 1;
constexpr size_t kDefaultBailoutDepth = 20;
@ -74,11 +74,11 @@ constexpr size_t kDefaultBailoutDepth = 20;
C10_DEFINE_int64(
torch_jit_num_profiled_runs,
kDefaultNumProfiledRuns,
"Number of profiling runs")
"Number of profiling runs");
C10_DEFINE_int64(
torch_jit_bailout_depth,
kDefaultBailoutDepth,
"Number of re-specializations")
"Number of re-specializations");
namespace torch::jit {

494
torch/csrc/jit/runtime/static/generated_ops.cpp
View File

File diff suppressed because it is too large Load Diff

2
torch/csrc/jit/runtime/static/impl.cpp
View File

@ -50,7 +50,7 @@
C10_DEFINE_bool(
static_runtime_disable_debug_memory_overlap_check,
false,
"If true, disable the memory overlap check in debug mode in ProcessedNode::run()")
"If true, disable the memory overlap check in debug mode in ProcessedNode::run()");
namespace torch::jit {

122
torch/csrc/jit/runtime/static/native_ops.cpp
View File

@ -72,7 +72,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
// put output back
p_node->Output(0) = std::move(stack[0]);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::TupleUnpack,
@ -91,7 +91,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(i) = elems[i];
}
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::DictConstruct,
@ -116,7 +116,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
p_node->Output(0) = result;
};
})
});
// See [Borrowed IValue Outputs]
REGISTER_NATIVE_OPERATOR_FUNCTOR(
@ -139,7 +139,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(i - 1) = createBorrowedIValue(value->value());
}
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::__getitem__, aten_getitem, [](Node* n) -> SROperator {
if (!sr_schema_check(
@ -177,7 +177,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::__getitem__, aten_getitem, [](Node* n) ->
// TODO(T98581096): make __getitem__ work for other container types
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::ListConstruct,
@ -197,7 +197,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
// put output back
p_node->Output(0) = std::move(stack[0]);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::ListUnpack,
@ -219,7 +219,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(i) = list[i];
}
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::append,
@ -233,7 +233,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
auto list = p_node->Input(0).toList();
list.push_back(p_node->Input(1));
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::list,
@ -260,7 +260,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::numel,
@ -273,7 +273,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& arg = p_node->Input(0).toTensor();
p_node->Output(0) = arg.numel();
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::cpu,
@ -286,7 +286,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& arg = p_node->Input(0).toTensor();
p_node->Output(0) = arg.cpu();
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::__range_length,
@ -312,7 +312,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(0) = 0;
}
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::index_put, aten_index_put, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -332,7 +332,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::index_put, aten_index_put, [](Node* n) ->
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::item,
@ -345,7 +345,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& self = p_node->Input(0).toTensor();
p_node->Output(0) = at::native::item(self);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::GetAttr,
@ -362,7 +362,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto slot = type.getAttributeSlot(field);
p_node->Output(0) = module.getSlot(slot);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::SetAttr,
@ -379,7 +379,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto slot = type.getAttributeSlot(field);
module.setSlot(slot, p_node->Input(1));
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::transpose,
@ -396,7 +396,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto in2_i = p_node->Input(2).toInt();
p_node->Output(0) = at::native::transpose(in0_t, in1_i, in2_i);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::flatten, aten_flatten, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -410,7 +410,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::flatten, aten_flatten, [](Node* n) -> SRO
const auto in2_i = p_node->Input(2).toInt();
p_node->Output(0) = at::native::flatten(in0_t, in1_i, in2_i);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::permute,
@ -426,7 +426,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto in1_iv = p_node->Input(1).toDimVector();
p_node->Output(0) = at::native::permute(in0_t, in1_iv);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::reshape,
@ -442,7 +442,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto in1_iv = p_node->Input(1).toDimVector();
p_node->Output(0) = at::native::reshape(in0_t, in1_iv);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::slice, aten_slice, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -458,7 +458,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::slice, aten_slice, [](Node* n) -> SROpera
const auto in4_i = p_node->Input(4).toInt();
p_node->Output(0) = at::native::slice(in0_t, in1_i, in2_i, in3_i, in4_i);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::narrow, aten_narrow, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -497,7 +497,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::narrow, aten_narrow, [](Node* n) -> SROpe
").");
p_node->Output(0) = at::native::slice(self, dim, start, start + length, 1);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::to, aten_to, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -544,7 +544,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::to, aten_to, [](Node* n) -> SROperator {
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::detach,
@ -559,7 +559,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& in0_t = p_node->Input(0).toTensor();
p_node->Output(0) = at::native::alias(in0_t);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::expand_as,
@ -575,7 +575,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& other = p_node->Input(1).toTensor();
p_node->Output(0) = self.expand(other.sizes());
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::isinstance,
@ -600,7 +600,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(0) = false;
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::TypeCheck,
@ -633,7 +633,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(num_inputs) = true;
};
})
});
// See [Borrowed IValue Outputs]
REGISTER_NATIVE_OPERATOR_FUNCTOR(
@ -653,7 +653,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
}
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::view,
@ -669,7 +669,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto size = p_node->Input(1).toIntList();
p_node->Output(0) = at::native::view(input, size.vec());
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::size,
@ -696,7 +696,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::squeeze,
@ -713,7 +713,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto dim = p_node->Input(1).toInt();
p_node->Output(0) = at::native::squeeze(self, dim);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::split, aten_split, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -739,7 +739,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::split, aten_split, [](Node* n) -> SROpera
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::split_with_sizes,
@ -759,7 +759,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(0) =
at::native::split_with_sizes(self, split_sizes.vec(), dim);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
static_runtime::select_tensor,
@ -788,7 +788,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
IValue(c10::MaybeOwnedTraits<at::TensorBase>::createBorrow(
assignFrom.toTensor()));
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::mul,
@ -814,7 +814,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
pnode->Output(0) = ret;
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::sub,
@ -829,7 +829,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto b = pnode->Input(1).toInt();
pnode->Output(0) = a - b;
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::add,
@ -855,7 +855,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::tensor_split, aten_tensor_split, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -889,7 +889,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(aten::tensor_split, aten_tensor_split, [](Node*
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::Int,
@ -903,7 +903,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& input = pnode->Input(0).toTensor();
pnode->Output(0) = at::native::item(input).toInt();
};
})
});
// See [Create owned refs for special values]
REGISTER_NATIVE_OPERATOR_FUNCTOR(
@ -915,7 +915,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
return
[](ProcessedNode* p_node) { p_node->Output(0) = p_node->Input(0); };
})
});
namespace {
bool outputsEmpty(const Block* block) {
@ -1020,7 +1020,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
return [](ProcessedNode*) {};
}
return [](ProcessedNode*) {};
})
});
namespace {
@ -1147,7 +1147,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
smodule, args, future, *launcher);
(*launcher)(std::move(runtime_launcher));
};
})
});
/*
aten::wait waits on the future (present in corresponding fork)
to be executed. Once the execution is complete, the future is marked
@ -1181,7 +1181,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(i) = elems[i];
}
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::Loop,
@ -1225,7 +1225,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
p_node->Output(i) = std::move(args[i + 1]);
}
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::CreateObject,
@ -1240,7 +1240,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
c10::StrongTypePtr(class_type->compilation_unit(), class_type),
class_type->numAttributes());
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::TupleIndex,
@ -1262,7 +1262,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
pnode->Output(0) = elems[norm_idx];
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::RaiseException,
@ -1275,7 +1275,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& message = pnode->Input(0).toStringRef();
throw std::runtime_error(message);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::Uninitialized,
@ -1287,7 +1287,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
return [](ProcessedNode* pnode) {
pnode->Output(0) = IValue::uninitialized();
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::format,
@ -1304,7 +1304,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
TORCH_DCHECK_EQ(stack.size(), 1);
pnode->Output(0) = std::move(stack[0]);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::device,
@ -1317,7 +1317,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& input = pnode->Input(0).toTensor();
pnode->Output(0) = input.device();
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::dtype,
@ -1330,7 +1330,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& input = pnode->Input(0).toTensor();
pnode->Output(0) = static_cast<int64_t>(input.scalar_type());
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::dim,
@ -1343,7 +1343,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& input = pnode->Input(0).toTensor();
pnode->Output(0) = input.dim();
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::__not__,
@ -1356,7 +1356,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
auto input = pnode->Input(0).toBool();
pnode->Output(0) = !input;
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::Bool,
@ -1382,7 +1382,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::is_cuda,
@ -1395,7 +1395,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& input = pnode->Input(0).toTensor();
pnode->Output(0) = input.is_cuda();
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
prim::tolist,
@ -1413,7 +1413,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
TORCH_DCHECK_EQ(stack.size(), 1);
pnode->Output(0) = std::move(stack[0]);
};
})
});
// See [Borrowed IValue Outputs]
REGISTER_NATIVE_OPERATOR_FUNCTOR(
@ -1428,7 +1428,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
pnode->Output(0) = condition ? createBorrowedIValue(pnode->Input(1))
: createBorrowedIValue(pnode->Input(2));
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::len,
@ -1474,7 +1474,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::IntImplicit,
@ -1500,7 +1500,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
}
pnode->Output(0) = at::native::item(tensor).toInt();
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::select,
@ -1517,7 +1517,7 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto index = pnode->Input(2).toInt();
pnode->Output(0) = at::native::select(self, dim, index);
};
})
});
REGISTER_NATIVE_OPERATOR_FUNCTOR(
aten::reshape_as,
@ -1533,6 +1533,6 @@ REGISTER_NATIVE_OPERATOR_FUNCTOR(
const auto& other = pnode->Input(1).toTensor();
pnode->Output(0) = at::native::reshape(self, other.sizes());
};
})
});
} // namespace torch::jit

147
torch/csrc/jit/runtime/static/ops.cpp
View File

@ -37,6 +37,8 @@
#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
#include <torch/csrc/jit/tensorexpr/loopnest.h>
#include <iterator>
#include <mutex>
#include <unordered_map>
#include <ATen/CompositeExplicitAutogradFunctions.h>
@ -44,9 +46,10 @@ C10_DEFINE_bool(
static_runtime_enable_fast_math,
true,
"If on, static runtime may use use optimizations that cause accuracy loss "
"vs the jit interpreter")
"vs the jit interpreter");
namespace at::native {
static void repeat_out(
at::Tensor& result,
const Tensor& self,
@ -137,9 +140,9 @@ static at::Tensor& flatten_copy_out(
// We don't want to infer_size on the entire shape, because that can give us
// an extra degree of freedom we don't want; for example, consider shape [0,
// 1, 3, 0], with start_dim=1, end_dim=2. It's clear we want result shape
// [0, 3, 0] but passing [0, -1, 0] to infer_size means the -1 can take on
// any value and satisfy the constraints.
// 1, 3, 0], with start_dim=1, end_dim=2. It's clear we want result shape [0,
// 3, 0] but passing [0, -1, 0] to infer_size means the -1 can take on any
// value and satisfy the constraints.
auto iter = self.sizes().data();
auto slice_numel = std::accumulate(
iter + start_dim,
@ -323,8 +326,8 @@ static Tensor& c2_argmin_out(
return true;
}
// if a is not nan and b is nan, then a is not less than b
// with LessOrNan semantics otherwise, act normally. If `b`
// is NaN then a < b will always return false, so this is
// with LessOrNan semantics otherwise, act normally. If `b` is
// NaN then a < b will always return false, so this is
// equivalent to the first snippet.
return a < b;
});
@ -375,7 +378,7 @@ static at::Tensor& dequantize_copy_out(Tensor& out, const Tensor& self) {
namespace torch::jit {
C10_DEFINE_REGISTRY(SROperatorRegistry, SROperatorFunctor)
C10_DEFINE_REGISTRY(SROperatorRegistry, SROperatorFunctor);
bool opIsRegistered(const c10::Symbol& op_name) {
const std::string name(op_name.toQualString());
@ -502,7 +505,7 @@ REGISTER_OPERATOR_FUNCTOR(
}
listConstructSlowPath(type, size, p_node);
};
})
});
static void tupleConstructSlowPath(const size_t size, ProcessedNode* p_node) {
// prepare inputs
@ -554,7 +557,7 @@ REGISTER_OPERATOR_FUNCTOR(
}
tupleConstructSlowPath(size, p_node);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::abs, aten_abs, [](Node* n) -> SROperator {
if (!n->matches(torch::schema("aten::abs(Tensor self) -> Tensor"))) {
@ -571,7 +574,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::abs, aten_abs, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::native::abs_out(in0_t, out_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::mul, aten_mul, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -591,7 +594,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::mul, aten_mul, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::cpu::mul_out(out_t, in0_t, in1_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::addmm, aten_addmm, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -613,7 +616,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::addmm, aten_addmm, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::cpu::addmm_out(out_t, in0_t, in1_t, in2_t, in3_s, in4_s);
};
})
});
#ifdef FBCODE_CAFFE2
// Disable externally to avoid MSVC errors in open-source CI
@ -673,9 +676,9 @@ REGISTER_OPERATOR_FUNCTOR(
&clamp_min,
&clamp_max,
&nan,
&output_size})
&output_size});
};
})
});
#endif
@ -720,7 +723,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::clamp, aten_clamp, [](Node* n) -> SROperator {
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(aten::bmm, aten_bmm, [](Node* n) -> SROperator {
if (!n->matches(
@ -738,7 +741,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::bmm, aten_bmm, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::cpu::bmm_out(out_t, in0_t, in1_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::nan_to_num, aten_nan_to_num, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -759,7 +762,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::nan_to_num, aten_nan_to_num, [](Node* n) -> SROp
fastResizeToZero(out_t);
at::native::nan_to_num_out(in0_t, in1_d, in2_d, in3_d, out_t);
};
})
});
namespace {
@ -892,7 +895,7 @@ static SROperator aten_stack(Node* n) {
};
}
REGISTER_OPERATOR_FUNCTOR(aten::stack, aten_stack, aten_stack)
REGISTER_OPERATOR_FUNCTOR(aten::stack, aten_stack, aten_stack);
REGISTER_OPERATOR_FUNCTOR(
prim::VarStack,
@ -910,7 +913,7 @@ REGISTER_OPERATOR_FUNCTOR(
}
varStackOut(*p_node, dim);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::leaky_relu, aten_leaky_relu, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -928,7 +931,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::leaky_relu, aten_leaky_relu, [](Node* n) -> SROp
auto& out_t = p_node->Output(0).toTensor();
at::cpu::leaky_relu_out(out_t, in0_t, in1_s);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::relu, aten_relu, [](Node* n) -> SROperator {
if (!n->matches(torch::schema("aten::relu(Tensor self) -> Tensor"))) {
@ -951,7 +954,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::relu, aten_relu, [](Node* n) -> SROperator {
int64_t nn = in0_t.numel();
te->call({out_t.data_ptr(), in0_t.data_ptr(), &nn});
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::tanh, aten_tanh, [](Node* n) -> SROperator {
if (!n->matches(torch::schema("aten::tanh(Tensor self) -> Tensor"))) {
@ -974,7 +977,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::tanh, aten_tanh, [](Node* n) -> SROperator {
int64_t nn = in0_t.numel();
te->call({out_t.data_ptr(), in0_t.data_ptr(), &nn});
};
})
});
REGISTER_OPERATOR_FUNCTOR(
prim::TensorExprDynamicGroup,
@ -1009,7 +1012,7 @@ REGISTER_OPERATOR_FUNCTOR(
}
}
};
})
});
REGISTER_OPERATOR_FUNCTOR(
aten::sigmoid,
@ -1035,7 +1038,7 @@ REGISTER_OPERATOR_FUNCTOR(
int64_t nn = in0_t.numel();
te->call({out_t.data_ptr(), in0_t.data_ptr(), &nn});
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::logit, aten_logit, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -1070,7 +1073,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::logit, aten_logit, [](Node* n) -> SROperator {
float c = clamp_value;
te->call({out_t.data_ptr(), in0_t.data_ptr(), &nn, &c});
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::clone, aten_clone, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -1111,7 +1114,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::clone, aten_clone, [](Node* n) -> SROperator {
out_t.unsafeGetTensorImpl(), src.sizes(), src.strides());
at::native::copy_(out_t, src, false);
};
})
});
REGISTER_OPERATOR_FUNCTOR(
quantized::embedding_bag_byte_rowwise_offsets,
@ -1149,7 +1152,7 @@ REGISTER_OPERATOR_FUNCTOR(
compressed_indices_mapping,
include_last_offset);
};
})
});
REGISTER_OPERATOR_FUNCTOR(
quantized::embedding_bag_4bit_rowwise_offsets,
@ -1187,7 +1190,7 @@ REGISTER_OPERATOR_FUNCTOR(
compressed_indices_mapping,
include_last_offset);
};
})
});
REGISTER_OPERATOR_FUNCTOR(
quantized::embedding_bag_byte_prepack,
@ -1208,7 +1211,7 @@ REGISTER_OPERATOR_FUNCTOR(
fastResizeToZero(out_t);
at::native::qembeddingbag_byte_prepack_out(out_t, weight);
};
})
});
// The out variant takes precedence over native
REGISTER_OPERATOR_FUNCTOR(aten::narrow_copy, aten_narrow_copy, [](Node* n) -> SROperator {
@ -1238,7 +1241,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::narrow_copy, aten_narrow_copy, [](Node* n) -> SR
fastResizeToZero(output);
at::native::narrow_copy_dense_cpu_out(self, dim, start, length, output);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::index, aten_index, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
"aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor"))) {
@ -1257,7 +1260,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::index, aten_index, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::cpu::index_out(out_t, in0_t, in1_l);
};
})
});
REGISTER_OPERATOR_FUNCTOR(
aten::index_select,
@ -1280,7 +1283,7 @@ REGISTER_OPERATOR_FUNCTOR(
fastResizeToZero(out);
at::native::index_select_out_cpu_(self, dim, index, out);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::pow, aten_pow, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -1342,7 +1345,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::pow, aten_pow, [](Node* n) -> SROperator {
}
LogAndDumpSchema(n);
return nullptr;
})
});
namespace {
@ -1620,7 +1623,7 @@ REGISTER_OPERATOR_FUNCTOR(
return to_maybe_copy_out_functor<false, false>;
}
}
})
});
// out variant takes precedence over native
// NB: This impl doesn't work for cpu->cuda copy/cast or vice versa.
@ -1643,7 +1646,7 @@ REGISTER_OPERATOR_FUNCTOR(
const bool has_memory_format = n->inputs().size() == 5;
return get_to_copy_functor(
has_constant_non_tensor_dtype_and_flags, has_memory_format);
})
});
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
REGISTER_OPERATOR_FUNCTOR(
@ -1668,7 +1671,7 @@ REGISTER_OPERATOR_FUNCTOR(
fastResizeToZero(out_t);
at::native::dequantize_copy_out(out_t, self);
};
})
});
// Out variants for view ops are registered to a separate registry because
// their outputs (views) can't participate in memory reuse.
@ -1692,7 +1695,7 @@ REGISTER_OPERATOR_FUNCTOR(
auto& out = p_node->Output(0).toTensor();
at::native::reshape_copy_out(out, self, proposed_shape, true);
};
})
});
REGISTER_OPERATOR_FUNCTOR(
static_runtime::flatten_copy,
@ -1715,7 +1718,7 @@ REGISTER_OPERATOR_FUNCTOR(
auto& out = p_node->Output(0).toTensor();
at::native::flatten_copy_out(out, self, start_dim, end_dim);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::sum, aten_sum, [](Node* n) -> SROperator {
if (n->inputs().size() != 2 && n->inputs().size() != 4) {
@ -1755,7 +1758,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::sum, aten_sum, [](Node* n) -> SROperator {
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(aten::mean, aten_mean, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -1792,7 +1795,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::mean, aten_mean, [](Node* n) -> SROperator {
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(aten::repeat, aten_repeat, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -1811,7 +1814,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::repeat, aten_repeat, [](Node* n) -> SROperator {
at::Tensor& output = p_node->Output(0).toTensor();
at::native::repeat_out(output, self, repeats);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::max, aten_max, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -1866,7 +1869,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::max, aten_max, [](Node* n) -> SROperator {
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(aten::sign, aten_sign, [](Node* n) -> SROperator {
if (!n->matches(torch::schema("aten::sign.Tensor(Tensor input) -> Tensor"))) {
@ -1883,7 +1886,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::sign, aten_sign, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::cpu::sign_out(out_t, in0_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::div, aten_div, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -1941,7 +1944,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::div, aten_div, [](Node* n) -> SROperator {
at::cpu::div_out(out_t, in0_t, in1_t, rounding_mode);
}
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::log, aten_log, [](Node* n) -> SROperator {
if (!n->matches(torch::schema("aten::log.Tensor(Tensor input) -> Tensor"))) {
@ -1958,7 +1961,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::log, aten_log, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::cpu::log_out(out_t, in0_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::sub, aten_sub, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -1994,7 +1997,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::sub, aten_sub, [](Node* n) -> SROperator {
}
LogAndDumpSchema(n);
return nullptr;
})
});
// TODO: support clamp_min.Tensor(Tensor self, Tensor min) -> Tensor
REGISTER_OPERATOR_FUNCTOR(
@ -2017,7 +2020,7 @@ REGISTER_OPERATOR_FUNCTOR(
fastResizeToZero(out_t);
at::cpu::clamp_min_out(out_t, in0_t, in1_s);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::argmin, aten_argmin, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2041,7 +2044,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::argmin, aten_argmin, [](Node* n) -> SROperator {
}
at::cpu::argmin_out(out_t, in0_t, dim, keepdim);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::softmax, aten_softmax, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2063,7 +2066,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::softmax, aten_softmax, [](Node* n) -> SROperator
dtype == at::ScalarType::Float;
at::cpu::_softmax_out(out_t, in_t, dim, half_to_float);
};
})
});
namespace {
@ -2119,7 +2122,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::layer_norm, aten_layer_norm, [](Node* n) -> SROp
at::Tensor& output = p_node->Output(0).toTensor();
at::native::layer_norm_cpu_out(output, *X, *gamma, *beta, eps, M, N);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::norm, aten_norm, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -2184,7 +2187,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::norm, aten_norm, [](Node* n) -> SROperator {
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(aten::matmul, aten_matmul, [](Node* n) -> SROperator {
if (!n->matches(
@ -2204,7 +2207,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::matmul, aten_matmul, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::native::matmul_out(in0_t, in1_t, out_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(quantized::linear, quantized_linear, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2246,7 +2249,7 @@ REGISTER_OPERATOR_FUNCTOR(quantized::linear, quantized_linear, [](Node* n) -> SR
input, output_scale, output_zero_point, out_t);
}
};
})
});
REGISTER_OPERATOR_FUNCTOR(
fb::quantized_linear,
@ -2293,7 +2296,7 @@ REGISTER_OPERATOR_FUNCTOR(
input, output_scale, output_zero_point, out_t);
}
};
})
});
namespace {
@ -2373,7 +2376,7 @@ REGISTER_OPERATOR_FUNCTOR(
return nullptr;
}
return quantized_linear_dynamic_fp16_impl<false>(n);
})
});
REGISTER_OPERATOR_FUNCTOR(
quantized::linear_relu_dynamic_fp16,
@ -2386,7 +2389,7 @@ REGISTER_OPERATOR_FUNCTOR(
return nullptr;
}
return quantized_linear_dynamic_fp16_impl<true>(n);
})
});
// device & pin_memory matter only when CUDA is enabled.
static bool hasTensorWithOptions(
@ -2435,7 +2438,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::full, aten_full, [](Node* n) -> SROperator {
p_node->Output(0) =
at::native::full_out(size, fill_value, p_node->Output(0).toTensor());
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::full_like, aten_full_like, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2461,7 +2464,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::full_like, aten_full_like, [](Node* n) -> SROper
at::native::resize_(out_t, in0_t.sizes(), std::nullopt);
at::native::fill_out(out_t, in1_s);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::ones, aten_ones, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2484,7 +2487,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::ones, aten_ones, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::native::ones_out(size, out_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::ones_like, aten_ones_like, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2509,7 +2512,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::ones_like, aten_ones_like, [](Node* n) -> SROper
fastResizeToZero(out_t);
at::native::ones_out(self.sizes(), out_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::zeros, aten_zeros, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2530,7 +2533,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::zeros, aten_zeros, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::compositeexplicitautograd::zeros_out(out_t, size);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::linear, aten_linear, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2552,7 +2555,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::linear, aten_linear, [](Node* n) -> SROperator {
fastResizeToZero(out_t);
at::native::linear_out(out_t, in0_t, in1_t, in2_t);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::linalg_norm, aten_linalg_norm, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -2602,7 +2605,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::linalg_norm, aten_linalg_norm, [](Node* n) -> SR
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(aten::cat, aten_cat, [](Node* n) -> SROperator {
if (!n->matches(
@ -2622,7 +2625,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::cat, aten_cat, [](Node* n) -> SROperator {
fastResizeToZero(output);
at::cpu::cat_outf(inputs, dim, output);
};
})
});
REGISTER_OPERATOR_FUNCTOR(aten::cumsum, aten_cumsum, [](Node* n) -> SROperator {
if (!n->matches(torch::schema(
@ -2642,7 +2645,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::cumsum, aten_cumsum, [](Node* n) -> SROperator {
fastResizeToZero(output);
at::cpu::cumsum_out(output, input, dim, dtype);
};
})
});
REGISTER_OPERATOR_FUNCTOR(
aten::nonzero,
@ -2662,7 +2665,7 @@ REGISTER_OPERATOR_FUNCTOR(
fastResizeToZero(output);
at::native::nonzero_out_cpu(input, output);
};
})
});
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
REGISTER_OPERATOR_FUNCTOR(
@ -2687,7 +2690,7 @@ REGISTER_OPERATOR_FUNCTOR(
fastResizeToZero(out_t);
at::cpu::cat_outf(inputs, dim, out_t);
};
})
});
namespace {
// This template and its specialization help us avoid compiler warnings
@ -2749,7 +2752,7 @@ REGISTER_OPERATOR_FUNCTOR(
int64_t nn = input.numel();
te->call({out.data_ptr(), input.data_ptr(), &nn});
};
})
});
REGISTER_OPERATOR_FUNCTOR(
aten::remainder,
@ -2787,7 +2790,7 @@ REGISTER_OPERATOR_FUNCTOR(
// Unrecognized overload
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(aten::where, aten_where, [](Node* n) -> SROperator {
if (n->matches(torch::schema(
@ -2808,7 +2811,7 @@ REGISTER_OPERATOR_FUNCTOR(aten::where, aten_where, [](Node* n) -> SROperator {
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(
prim::NumToTensor,
@ -2830,7 +2833,7 @@ REGISTER_OPERATOR_FUNCTOR(
}
LogAndDumpSchema(n);
return nullptr;
})
});
REGISTER_OPERATOR_FUNCTOR(
quantized::embedding_bag_byte_unpack,
@ -2852,6 +2855,6 @@ REGISTER_OPERATOR_FUNCTOR(
auto& out = pnode->Output(0).toTensor();
at::native::qembeddingbag_byte_unpack_out(out, weight);
};
})
});
} // namespace torch::jit

15
torch/csrc/jit/runtime/static/passes.cpp
View File

@ -12,9 +12,10 @@
C10_DEFINE_bool(
enable_clip_ranges_gather_fusions,
true,
"If on, static runtime or optimize_sparse_nn_model will fuse clip ranges gather ops.")
"If on, static runtime or optimize_sparse_nn_model will fuse clip ranges gather ops.");
namespace torch::jit {
bool graphHasOp(std::shared_ptr<Graph>& graph, const char* op_name) {
DepthFirstGraphNodeIterator graph_it(graph);
for (auto node = graph_it.next(); node != nullptr; node = graph_it.next()) {
@ -714,8 +715,8 @@ static void ReplaceWithCopyImpl(
// b and c are aliases of a, sigmoid_ changes b, c, as well as a. e should
// equal to d in this case. If we replace reshape with the copy version, b
// and c are no longer aliases of a, the value of e would change as a
// result. To keep static runtime consistent with the jit interpreter,
// here we choose not to replace reshape with the copy version
// result. To keep static runtime consistent with the jit interpreter, here
// we choose not to replace reshape with the copy version
if (db.hasInputWriters(n)) {
continue;
}
@ -1085,8 +1086,8 @@ void ForceNonEmptyOutputsHelper(Value* none_value, Block* block) {
}
if (needs_output) {
// Loop sub-blocks should always return at least one output (the new
// loop condition)
// Loop sub-blocks should always return at least one output (the new loop
// condition)
DCHECK(node->kind() == prim::If);
auto* output = node->addOutput();
output->setType(c10::NoneType::get());
@ -1339,8 +1340,8 @@ bool isNoOpSlice(Node* node) {
return false;
}
auto end = toIValue(node->input(2));
// Could also look at list length, but most models that have this pattern
// are just doing list[0:], so it's not needed for now.
// Could also look at list length, but most models that have this pattern are
// just doing list[0:], so it's not needed for now.
return end.has_value() && end->isNone();
}
} // namespace

10
torch/csrc/jit/testing/file_check.cpp
View File

@ -538,7 +538,7 @@ struct FileCheckImpl {
std::vector<std::vector<Check>> groups;
};
FileCheck::FileCheck() : fcImpl(new FileCheckImpl()) {}
FileCheck::FileCheck() : fcImpl(new FileCheckImpl()){};
std::ostream& operator<<(std::ostream& out, const FileCheckImpl& fc) {
out << "FileCheck checks:\n";
@ -546,7 +546,7 @@ std::ostream& operator<<(std::ostream& out, const FileCheckImpl& fc) {
out << "\t" << c << "\n";
}
return out;
}
};
FileCheck::~FileCheck() {
if (!fcImpl->has_run) {
@ -554,17 +554,17 @@ FileCheck::~FileCheck() {
std::cout << *fcImpl;
}
fcImpl.reset();
}
};
void FileCheck::run(const std::string& test_file) {
fcImpl->run(test_file);
}
};
void FileCheck::run(const Graph& graph) {
std::stringstream graph_str;
graph_str << graph;
fcImpl->run(graph_str.str());
}
};
void FileCheck::run(
const std::string& input_checks_string,

34
torch/csrc/lazy/core/config.cpp
View File

@ -6,74 +6,74 @@ C10_DEFINE_bool(torch_lazy_ir_debug, false, "Enable lazy tensor IR debugging");
C10_DEFINE_bool(
torch_lazy_param_aliasing,
true,
"Enable parameter aliasing support")
"Enable parameter aliasing support");
C10_DEFINE_bool(
torch_lazy_handle_special_scalars,
false,
"Handle special scalars 0 and 1 differently")
"Handle special scalars 0 and 1 differently");
C10_DEFINE_bool(
torch_lazy_all_numbers_special_scalars,
false,
"Handle all numbers as special scalars")
"Handle all numbers as special scalars");
C10_DEFINE_bool(
torch_lazy_reuse_ir,
false,
"Reuse IR nodes from previous tracing when possible")
"Reuse IR nodes from previous tracing when possible");
C10_DEFINE_bool(
torch_lazy_use_thread_pool,
false,
"Use thread pool to schedule backend execution")
"Use thread pool to schedule backend execution");
C10_DEFINE_bool(
torch_lazy_enable_device_data_cache,
true,
"Enable or disable device data cache (turns cache on or off), does not change cache state")
"Enable or disable device data cache (turns cache on or off), does not change cache state");
C10_DEFINE_int(
torch_lazy_compilation_cache_size,
1024,
"Size of the compilation cache")
"Size of the compilation cache");
C10_DEFINE_int(
torch_lazy_device_data_cache_size,
128,
"Size of the DeviceData cache")
"Size of the DeviceData cache");
C10_DEFINE_int(
torch_lazy_io_thread_pool_size,
// TODO: measure which default value
// will give better performance,
// std::thread::hardware_concurrency()?
// TODO: measure which default value will give better
// performance, std::thread::hardware_concurrency()?
1,
"Size of the execution thread pool")
"Size of the execution thread pool");
C10_DEFINE_int(torch_lazy_metrics_samples, 1024, "Max metrics sample size")
C10_DEFINE_int(torch_lazy_metrics_samples, 1024, "Max metrics sample size");
C10_DEFINE_int(
torch_lazy_trim_graph_check_frequency,
5000,
"How often to check for whether a graph needs to be split")
"How often to check for whether a graph needs to be split");
C10_DEFINE_int(
torch_lazy_trim_graph_size,
100000,
"The threshold (in terms of the number of nodes) for splitting a graph")
"The threshold (in terms of the number of nodes) for splitting a graph");
C10_DEFINE_string(
torch_lazy_metrics_percentiles,
"0.01:0.05:0.1:0.2:0.5:0.8:0.9:0.95:0.99",
"Metrics percentiles to be collected, using : as the delimiter")
"Metrics percentiles to be collected, using : as the delimiter");
C10_DEFINE_int(
torch_lazy_shape_cache_size,
4096,
"Set the size for the shape cache used for shape inference")
"Set the size for the shape cache used for shape inference");
namespace torch::lazy {
std::string& getLTCForceFallback() {
static std::string config;
static bool _ignore = [&]() {

6
torch/csrc/lazy/core/dynamic_ir.h
View File

@ -35,13 +35,13 @@ class TORCH_API DimensionNode {
public:
virtual bool isSymbolic() const {
return false;
}
};
virtual int64_t getDynamicValue() const {
TORCH_CHECK(false, "NYI");
}
};
virtual int64_t getStaticValue() const {
TORCH_CHECK(false, "NYI");
}
};
virtual ~DimensionNode() = default;
};

3
torch/csrc/lazy/core/ir.cpp
View File

@ -10,9 +10,10 @@
C10_DEFINE_bool(
ltc_enable_dynamic_shapes,
false,
"Whether dynamic shape is enabled")
"Whether dynamic shape is enabled");
namespace torch::lazy {
static const torch::lazy::Output kNullOutput = torch::lazy::Output();
size_t Output::Hasher::operator()(const Output& output) const {

8
torch/csrc/lazy/ts_backend/dynamic_ir.cpp
View File

@ -30,7 +30,7 @@ SizeNode::SizeNode(Value input, size_t dim)
std::vector<Shape>{},
1,
MHash(dim)),
dim_(dim) {}
dim_(dim){};
int64_t SizeNode::getStaticValue() const {
return dynamic_cast<const TsNode*>(operand(0).node)
@ -55,7 +55,7 @@ SizeAdd::SizeAdd(Value a, Value b)
OpKind{c10::Symbol::fromQualString("aten::add")},
{std::move(a), std::move(b)},
std::vector<Shape>{},
1) {}
1){};
int64_t SizeAdd::getStaticValue() const {
return DimCast(operand(0))->getStaticValue() +
@ -75,7 +75,7 @@ SizeMul::SizeMul(Value a, Value b)
OpKind{c10::Symbol::fromQualString("aten::mul")},
{std::move(a), std::move(b)},
std::vector<Shape>{},
1) {}
1){};
int64_t SizeMul::getStaticValue() const {
return DimCast(operand(0))->getStaticValue() *
@ -95,7 +95,7 @@ SizeDiv::SizeDiv(Value a, Value b)
OpKind{c10::Symbol::fromQualString("aten::div")},
{std::move(a), std::move(b)},
std::vector<Shape>{},
1) {}
1){};
int64_t SizeDiv::getStaticValue() const {
TORCH_CHECK(

2
torch/csrc/lazy/ts_backend/ts_native_functions.cpp
View File

@ -268,7 +268,7 @@ at::Tensor LazyNativeFunctions::_to_copy(
std::move(node), lazy_self->GetDevice()));
return result;
}
}
};
at::Tensor LazyNativeFunctions::empty_symint(
at::SymIntArrayRef sym_size,

2
torchgen/executorch/api/custom_ops.py
View File

@ -129,7 +129,7 @@ def gen_custom_ops_registration(
static_init_dispatch_registrations += f"""
TORCH_LIBRARY_IMPL({namespace}, {dispatch_key}, m) {{
{dispatch_registrations_body}
}}"""
}};"""
anonymous_definition = "\n".join(
list(
concatMap(

2
torchgen/gen.py
View File

@ -1615,7 +1615,7 @@ def get_native_function_definitions(
registration_body += f"""
TORCH_LIBRARY_IMPL({namespace}, {dispatch_key}, m) {{
{newline.join(registrations[kernel_namespace][namespace])}
}}"""
}};"""
definitions.extend(
fm.substitute_with_template(
"RegisterDispatchDefinitions.ini",

2
torchgen/gen_backend_stubs.py
View File

@ -460,7 +460,7 @@ def gen_dispatcher_registrations(
"""\
TORCH_LIBRARY_IMPL(aten, $dispatch_key, m) {
$dispatch_registrations_body
}"""
};"""
)
static_init_dispatch_registrations = static_template.substitute(
dispatch_key=dispatch_key,